​[[{“value”:” Contrastive image and text models face significant challenges in optimizing retrieval accuracy despite their crucial role in large-scale text-to-image and image-to-text retrieval systems. While these models effectively learn joint embeddings through contrastive loss functions to align matching text-image pairs and separate non-matching pairs, they… Read More »Nearest Neighbor Normalization: A Sublinear Approach to Improving Contrastive Retrieval Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​In real life, the machine learning model is not a standalone object that only produces a prediction. In real life, the machine learning model is not a standalone object that only produces a prediction.  Read More  

​[[{“value”:” The landscape of AI research is experiencing significant challenges due to the immense computational requirements of large pre-trained language and vision models. Training even relatively modest models demand substantial resources; for instance, Pythia-1B requires 64 GPUs for three days, while RoBERTa needs 1,000 GPUs… Read More »Optimizing Large-Scale AI Model Pre-Training for Academic Research: A Resource-Efficient Approach Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The advent of AI has revolutionized the landscape of graphic design. AI graphic design tools are reshaping the way designers work, offering unprecedented efficiency, creativity, and innovation. These tools can automate repetitive tasks, generate fresh ideas, and accelerate the design process, empowering designers to… Read More »Top 20 AI Graphic Design Tools in 2025 Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Recurrent Neural Networks were the trailblazers in natural language processing and set the cornerstone for future advances. RNNs were simple in structure with their contextual memory and constant state size, which promised the capacity to handle long sequence tasks. While theoretically, the design of… Read More »This AI Research Diagnoses Problems in Recurrent Neural Networks RNN-based Language Models and Corrects them to Outperform Transformer-based Models on Long Sequence Tasks Adeeba Alam Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Foundation models show impressive capabilities across tasks and modalities, outperforming traditional AI approaches often task-specific and limited by modality. In medicine, however, developing such models faces challenges due to restricted access to diverse data and strict privacy laws. While capable in specific areas, existing… Read More »FEDKIM: A Federated Knowledge Injection Framework for Enhancing Multimodal Medical Foundation Models Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The use of large language models like GPT-4o and GPT-4o-mini has brought significant advancements in natural language processing, enabling high-quality response generation, document rewriting, and productivity enhancements across numerous applications. However, one of the biggest challenges these models face is latency. Whether it’s updating… Read More »OpenAI Introduces ‘Predicted Outputs’ Feature: Speeding Up GPT-4o by ~5x for Tasks like Editing Docs or Refactoring Code Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In recent years, the field of text-to-speech (TTS) synthesis has seen rapid advancements, yet it remains fraught with challenges. Traditional TTS models often rely on complex architectures, including deep neural networks with specialized modules such as vocoders, text analyzers, and other adapters to synthesize… Read More »OuteTTS-0.1-350M Released: A Novel Text-to-Speech (TTS) Synthesis Model that Leverages Pure Language Modeling without External Adapters Asif Razzaq Artificial Intelligence Category – MarkTechPost

​Neural contextual biasing allows speech recognition models to leverage contextually relevant information, leading to improved transcription accuracy. However, the biasing mechanism is typically based on a cross-attention module between the audio and a catalogue of biasing entries, which means computational complexity can pose severe practical… Read More »Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval Apple Machine Learning Research

​[[{“value”:”This paper was accepted at the Adaptive Foundation Models (AFM) workshop at NeurIPS Workshop 2024. Follow-up conversations with virtual assistants (VAs) enable a user to seamlessly interact with a VA without the need to repeatedly invoke it using a keyword (after the first query). Therefore,… Read More »Device-Directed Speech Detection for Follow-up Conversations Using Large Language Models Apple Machine Learning Research